Abdelhadi, Ahmed2023-07-092023-07-09May 20232023-05-15Portions of this document appear in: Shtaiwi, Eyad, Ahmed El Ouadrhiri, Majid Moradikia, Salma Sultana, Ahmed Abdelhadi, and Zhu Han. "Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks." In GLOBECOM 2022-2022 IEEE Global Communications Conference, pp. 1472-1477. IEEE, 2022.https://hdl.handle.net/10657/14936Deep learning (DL) has proven to be highly effective in solving classification problems, making it an ideal tool for identifying unknown modulation signals. This research aims to accurately classify signal modulation classes in over-the-air approaches using software-defined radios (SDR). The study examines a wireless communication system with a transmitter (Tx) and receiver (Rx). The process of Modulation Classification at the Rx is achieved by utilizing deep learning models such as the Convolutional Neural Network (CNN), Residual Network (ResNet), and a combination of CNN and Long Short-Term Memory (CLSTM). These models classify the modulated signals, like BPSK, QPSK, 8PSK, and 16QAM. To introduce adversarial attacks, an adversary (Ad) is added to the communication system. In this report, the Fast-Gradient Sign method (FGSM) adversarial attack method is employed to create fake signals that deceive DL-based classifiers and lead to significant reductions in the accuracies of three DL models. To combat adversarial attacks, a generative-adversarial network (GAN)-based defense is proposed to enhance the accuracy of the DL models.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Deep learningAdversarial attackGenerative adversarial networks2023-07-09Thesisborn digital